Data Analysis | SQL,Tableau,Power BI & Excel | Real Projects

Explore the data engineer, data scientist, and data analyst roles and the data collection, cleaning, exploration, analysis, visualization, and reporting process, using SQL, Excel, Tableau, and Power BI.

Turn raw Netflix data into a relational database in Excel by creating tables with primary keys, defining one-to-many relationships, and applying normalization to reduce repetition and improve data integrity.

Explore an optional Netflix titles project at the end of the course to practice relational databases with normalization, multiple tables, and primary and foreign keys visualized in an air diagram.

Explore how to build SQL queries using select, from, and where clauses, apply conditions with equals, not equals, and or, and handle strings enclosed in quotes.

Learn how to perform case-insensitive searches in SQL using upper and lower in where clauses, ensuring Leslie or D Murphy match regardless of data case, with examples in MySQL Workbench.

Learn to use in and not in to filter queries, apply upper for case-insensitive matching, and simplify multi-value filters instead of multiple or conditions.

Learn to use distinct to obtain unique values, apply like with wildcards for pattern matching, and order by to sort results ascending or descending, including multi-column ordering.

Learn how to combine customers and payments tables using inner and left joins, handle duplicates, and identify customers with no payments through is null filters.

Master count, max, min, and avg to summarize sql data. Count distinct order numbers and group by product code, then compute daily max, min, and average payments.

Learn how to use case statements to flag orders with negotiate or dispute in the comments, creating flags and strings for reporting and analysis in SQL queries.

Explore the row number window function, assigning a sequential number to rows by partition and order, with SQL examples, joins, and CTEs.

Explore lead and lag window functions to access next or previous payments within each customer's ordered payments, using over partition by and order by, and compute differences with a CTE.

Learn to extract year, month, and day from date fields, compute days between order and required dates with date diff, and use date add and date sub to adjust dates.

Explore variance and standard deviation as measures of data dispersion and risk, illustrated with stock prices, mean, squared deviations, and the interpretation of volatility.

Master frequency distributions to organize data by counting frequencies and choosing between ungrouped and grouped formats. Visualize patterns with bar charts using grade or age buckets.

Explore the distinction between quantitative and qualitative variables, including discrete and continuous, nominal and ordinal types, and see how these classifications guide data analysis.

Master essential excel shortcuts for navigating and editing a Netflix spreadsheet, including ctrl+home, ctrl+end, shift+arrows, and page up/down sheet navigation.

Split text to columns to separate director, country, and listed in fields using comma as the delimiter. Rename and tidy the resulting columns, and next, explore the trim function.

Trim and separate data across sheets using the trim function, create show ID relationships, and paste values to prepare cast, directors, and countries for import.

Learn to sort and filter data by splitting the duration into minutes and seasons with text to columns, then filter to minutes and sort by duration from largest to smallest.

Learn to use sumifs and countifs to sum or count a range with multiple criteria, such as movie type and release year, including 2020 and 2021.

Explore using unique, left, and right to surface distinct values and extract starting or ending characters, handle messy data, and preview durations and years before learning Vlookup.

Learn to use Xlookup in Excel to retrieve a description by show ID from another table, replacing Vlookup, and apply the formula across rows.

Download Power BI and connect to data, loading Netflix data from MySQL. Build a titles table with seven columns and learn the data connection workflow.

Analyze 2004 sales by product using SQL joins across orders, order details, customers, and products, showing sales value, cost of sales, and net profit by city and country.

Identify orders affected by late shipping by adding three days to ship dates, create a late flag with a case statement, and filter results to show only delayed shipments.

Use ChatGPT to extract insights by product, country, city, detailing sales value, cost of sales, and net profit for 2004. Learn prompt design, table schemas, and SQL-based validation of results.

Demonstrate building intermediate and advanced SQL queries with ChatGPT in MySQL, including lag over partition by to show customer sales, previous sale value, differences, and money owed with payments.

Create a view in MySQL Workbench by joining orders, order details, customers, products, employees, and offices; compute sales value and cost of sales; connect view to Power BI for dashboard.

Create overview line charts showing monthly sales by office (USA vs rest of world), with three panels for total sales, count of unique orders, and average order value.

Learn to build interactive Power BI dashboards by linking buttons to bookmarks, adding slicers, and displaying a selected metric in a clustered bar chart with currency data labels.

Create and compare charts by duplicating visuals and switching to scatter, column, and donut types to analyze cost of sale, net profit, and country-level performance across orders.

Learn to conditionally format chart titles to show sales or net profit via a control. Set subtitles, axis titles, and styling to build a clear, interactive dashboard.

Add a Power BI sales overview table showing year, month, sales value, month-over-month change, and year-to-date total, with quick measures, formatting, and data cleaning steps.

Explore cloud computing and Snowflake, a cloud-based data warehouse for analytics. Set up a Snowflake account, run SQL queries in the browser, and compare cloud databases with local MySQL Workbench.

Plan data ingestion into Snowflake by choosing batch or streaming based on frequency and size. Stage data internally or externally and load with bulk loading or Snowpipe for real-time access.

Create a Snowflake stage for the customer details schema, enable client-side encryption, upload json, parquet, and csv files, and load the csv into a table named customer csv.

Learn how to load json and parquet data into Snowflake from stages, create and modify tables, cast date fields, and ensure correct column mapping with case-insensitive matching.

Analyze a Disneyland review dataset across California, Hong Kong, and Paris to extract sentiment and summarize themes. Load the data into Snowflake and apply Cortex to derive actionable insights.

Understand the Snowflake Cortex sentiment function, which outputs a float between -1 and 1 by analyzing a text column to reveal review sentiment and enable threshold-based filtering.

Discover how ai_agg aggregates a text column to extract insights, returning the six most common categories from review text using a simple prompt, with options to filter by sentiment.

Explore text classification in Snowflake cortex by defining categories, classifying reviews into single or multiple labels, and extracting labeled outputs from JSON results.

Learn to unpack a JSON categories array in Snowflake with lateral flatten, producing one row per category and sentiment per review for easy grouping and counting.